I guess my question its a little strange, let me try to explain it. I need to solve a simple equation for a longitudinal database (29 consecutive years) about food availability and international commerce: (importations-exportations)/(production+importations-exportations)*100[equation for food dependence coeficient, by FAO]. The big problem is that my database has the food products and its values of interest (production, importation and exportation) dissagregated, so i need to find a way to apply that equation to a sum of the values of interest for every year, so i can get the coeficient i need for every year.
My data frame looks like this:
element product year value (metric tons)
Production Wheat 1990 16
Importation Wheat 1990 2
Exportation Wheat 1990 1
Production Apples 1990 80
Importation Apples 1990 0
Exportation Apples 1990 72
Production Wheat 1991 12
Importation Wheat 1991 20
Exportation Wheat 1991 0
I guess the solution its pretty simple, but im not good enough in R to solve this problem by myself. Every help is very welcome.
Thanks!
This is a picture of my R session
require(data.table)
# dummy table. Use setDT(df) if yours isn't a data table already
df <- data.table(element = (rep(c('p', 'i', 'e'), 3))
, product = (rep(c('w', 'a', 'w'), each=3))
, year = rep(c(1990, 1991), c(6,3))
, value = c(16,2,1,80,0,72,12,20,0)
); df
element product year value
1: p w 1990 16
2: i w 1990 2
3: e w 1990 1
4: p a 1990 80
5: i a 1990 0
6: e a 1990 72
7: p w 1991 12
8: i w 1991 20
9: e w 1991 0
# long to wide
df_1 <- dcast(df
, product + year ~ element
, value.var = 'value'
); df_1
# apply calculation
df_1[, food_depend_coef := (i-e) / (p+i-e)*100][]
product year e i p food_depend_coef
1: a 1990 72 0 80 -900.000000
2: w 1990 1 2 16 5.882353
3: w 1991 0 20 12 62.500000
Related
*Update: The answer suggested by Rui is great and works as it should. However, when I run it on about 7 million observations (my actual dataset), R gets stuck in a computational block (I'm using a machine with 64gb of RAM). Any other solutions are greatly appreciated!
I have a dataframe of patents consisting of the firms, application years, patent number, and patent classes. I want to calculate the Euclidean distance between consecutive years for each firm based on patent classes according to the following formula:
Where Xi represents the number of patents belonging to a specific class in year t, and Yi represents the number of patents belonging to a specific class in the previous year (t-1).
To further illustrate this, consider the following dataset:
df <- data.table(Firm = rep(c(LETTERS[1:2]),each=6), Year = rep(c(1990,1990,1991,1992,1992,1993),2),
Patent_Number = sample(184785:194785,12,replace = FALSE),
Patent_Class = c(12,5,31,12,31,6,15,15,15,3,3,1))
> df
Firm Year Patent_Number Patent_Class
1: A 1990 192473 12
2: A 1990 193702 5
3: A 1991 191889 31
4: A 1992 193341 12
5: A 1992 189512 31
6: A 1993 185582 6
7: B 1990 190838 15
8: B 1990 189322 15
9: B 1991 190620 15
10: B 1992 193443 3
11: B 1992 189937 3
12: B 1993 194146 1
Since year 1990 is the beginning year for Firm A, there is no Euclidean distance for that year (NAs should be produced. Moving forward to year 1991, the distinct classses for this year (1991) and the previous year (1990) are 31, 5, and 12. Therefore, the above formula is summed over these three distinct classes (there is three distinc 'i's). So the formula's output will be:
Following the same calculation and reiterating over firms, the final output should be:
> df
Firm Year Patent_Number Patent_Class El_Dist
1: A 1990 192473 12 NA
2: A 1990 193702 5 NA
3: A 1991 191889 31 1.2247450
4: A 1992 193341 12 0.7071068
5: A 1992 189512 31 0.7071068
6: A 1993 185582 6 1.2247450
7: B 1990 190838 15 NA
8: B 1990 189322 15 NA
9: B 1991 190620 15 0.5000000
10: B 1992 193443 3 1.1180340
11: B 1992 189937 3 1.1180340
12: B 1993 194146 1 1.1180340
I'm preferably looking for a data.table solution for speed purposes.
Thank you very much in advance for any help.
I believe that the function below does what the question asks for, but the results for Firm == "B" are not equal to the question's.
fEl_Dist <- function(X){
Year <- X[["Year"]]
PatentClass <- X[["Patent_Class"]]
sapply(seq_along(Year), function(i){
j <- which(Year %in% (Year[i] - 1:0))
tbl <- table(Year[j], PatentClass[j])
if(NROW(tbl) == 1){
NA_real_
} else {
numer <- sum((tbl[2, ] - tbl[1, ])^2)
denom <- sum(tbl[2, ]^2)*sum(tbl[1, ]^2)
sqrt(numer/denom)
}
})
}
setDT(df)[, El_Dist := fEl_Dist(.SD),
by = .(Firm),
.SDcols = c("Year", "Patent_Class")]
head(df)
# Firm Year Patent_Number Patent_Class El_Dist
#1: A 1990 190948 12 NA
#2: A 1990 186156 5 NA
#3: A 1991 190801 31 1.2247449
#4: A 1992 185226 12 0.7071068
#5: A 1992 185900 31 0.7071068
#6: A 1993 186928 6 1.2247449
I would like to create a variable called spill which is given as the sum of the distances between vectors of each row multiplied by the stock value. For example, consider
firm us euro asia africa stock year
A 1 4 3 5 46 2001
A 2 0 1 3 889 2002
B 2 3 1 1 343 2001
B 0 2 1 3 43 2002
C 1 3 4 2 345 2001
I would like to create a vector which basically takes the distance between two firms at time t and generates the spill variable. For example, take for Firm A in the year 2001 it would be 0.204588 (which is the cosine distance between firm A and B at time t i.e, in 2001 (1,4,3,5) and (2,3,1,1) (i.e. similarity between the investments in us, euro, asia, africa) and then multiplied by 343, and then to calculate the distance between A and C in 2001 as .10528 * 345 , hence the spill variable is = 0.2045883 * 343+ 0.1052075 * 345 = 106.4704 for the year 2001 for firm A.
I want to get a table including spill like this
firm us euro asia africa stock year spill
A 1 4 3 5 46 2001 106.4704
A 2 0 1 3 889 2002
B 2 3 1 1 343 2001
B 0 2 1 3 43 2002
C 1 3 4 2 345 2001
Can anyone please advise?
Here are the codes for stata[https://www.statalist.org/forums/forum/general-stata-discussion/general/1409182-calculating-distance-between-two-variables-and-generating-new-variable]. I have about 3,000 firms and 30 years. It runs well but very slowly.
dt <- data.frame(id=c("A","A","B","B","C"),us=c(1,2,2,0,1),euro=c(4,0,3,2,3),asia=c(3,1,1,1,4),africa=c(5,3,1,3,2),stock=c(46,889,343,43,345),year=c(2001,2002,2001,2002,2001))
Given the minimal info on how to calculate the similarity distance I've used a formula from Find cosine similarity between two arrays which will return different numbers than yours but should give the same resulting info.
I split the data by year so we can compare the unique ids. I take those individual lists and use lapply to run a for loop comparing all possibilities.
dt <- data.frame(id=c("A","A","B","B","C"), us=c(1,2,2,0,1),euro=c(4,0,3,2,3),asia=c(3,1,1,1,4),africa=c(5,3,1,3,2),stock=c(46,889,343,43,345),year=c(2001,2002,2001,2002,2001))
geo <- c("us","euro","asia","africa")
s <- lapply(split(dt, dt$year), function(a) {
n <- nrow(a)
for(i in 1:n){
csim <- rep(0, n) # reset results of cosine similarity *stock vector
for(j in 1:n){
x <- unlist(a[i,geo])
y <- unlist(a[j,geo])
csim[j] <- (1-(x %*% y / sqrt(x%*%x * y%*%y)))*a[j,"stock"]
}
a$spill[i] <- sum(csim)
}
a
})
do.call(rbind, s)
# id us euro asia africa stock year spill
#2001.1 A 1 4 3 5 46 2001 106.47039
#2001.3 B 2 3 1 1 343 2001 77.93231
#2001.5 C 1 3 4 2 345 2001 72.96357
#2002.2 A 2 0 1 3 889 2002 12.28571
#2002.4 B 0 2 1 3 43 2002 254.00000
I have tried to find a solution via similar topics, but haven't found anything suitable. This may be due to the search terms I have used. If I have missed something, please accept my apologies.
Here is a excerpt of my data UN_ (the provided sample should be sufficient):
country year sector UN
AT 1990 1 1.407555
AT 1990 2 1.037137
AT 1990 3 4.769618
AT 1990 4 2.455139
AT 1990 5 2.238618
AT 1990 Total 7.869005
AT 1991 1 1.484667
AT 1991 2 1.001578
AT 1991 3 4.625927
AT 1991 4 2.515453
AT 1991 5 2.702081
AT 1991 Total 8.249567
....
BE 1994 1 3.008115
BE 1994 2 1.550344
BE 1994 3 1.080667
BE 1994 4 1.768645
BE 1994 5 7.208295
BE 1994 Total 1.526016
BE 1995 1 2.958820
BE 1995 2 1.571759
BE 1995 3 1.116049
BE 1995 4 1.888952
BE 1995 5 7.654881
BE 1995 Total 1.547446
....
What I want to do is, to add another row with UN_$sector = Residual. The value of residual will be (UN_$sector = Total) - (the sum of column UN for the sectors c("1", "2", "3", "4", "5")) for a given year AND country.
This is how it should look like:
country year sector UN
AT 1990 1 1.407555
AT 1990 2 1.037137
AT 1990 3 4.769618
AT 1990 4 2.455139
AT 1990 5 2.238618
----> AT 1990 Residual TO BE CALCULATED
AT 1990 Total 7.869005
As I don't want to write many, many lines of code I'm looking for a way to automate this. I was told about loops, but can't really follow the concept at the moment.
Thank you very much for any type of help!!
Best,
Constantin
PS: (for Parfait)
country year sector UN ETS
UK 2012 1 190336512 NA
UK 2012 2 18107910 NA
UK 2012 3 8333564 NA
UK 2012 4 11269017 NA
UK 2012 5 2504751 NA
UK 2012 Total 580957306 NA
UK 2013 1 177882200 NA
UK 2013 2 20353347 NA
UK 2013 3 8838575 NA
UK 2013 4 11051398 NA
UK 2013 5 2684909 NA
UK 2013 Total 566322778 NA
Consider calculating residual first and then stack it with other pieces of data:
# CALCULATE RESIDUALS BY MERGED COLUMNS
agg <- within(merge(aggregate(UN ~ country + year, data = subset(df, sector!='Total'), sum),
aggregate(UN ~ country + year, data = subset(df, sector=='Total'), sum),
by=c("country", "year")),
{UN <- UN.y - UN.x
sector = 'Residual'})
# ROW BIND DIFFERENT PIECES
final_df <- rbind(subset(df, sector!='Total'),
agg[c("country", "year", "sector", "UN")],
subset(df, sector=='Total'))
# ORDER ROWS AND RESET ROWNAMES
final_df <- with(final_df, final_df[order(country, year, as.character(sector)),])
row.names(final_df) <- NULL
Rextester demo
final_df
# country year sector UN
# 1 AT 1990 1 1.407555
# 2 AT 1990 2 1.037137
# 3 AT 1990 3 4.769618
# 4 AT 1990 4 2.455139
# 5 AT 1990 5 2.238618
# 6 AT 1990 Residual -4.039062
# 7 AT 1990 Total 7.869005
# 8 AT 1991 1 1.484667
# 9 AT 1991 2 1.001578
# 10 AT 1991 3 4.625927
# 11 AT 1991 4 2.515453
# 12 AT 1991 5 2.702081
# 13 AT 1991 Residual -4.080139
# 14 AT 1991 Total 8.249567
# 15 BE 1994 1 3.008115
# 16 BE 1994 2 1.550344
# 17 BE 1994 3 1.080667
# 18 BE 1994 4 1.768645
# 19 BE 1994 5 7.208295
# 20 BE 1994 Residual -13.090050
# 21 BE 1994 Total 1.526016
# 22 BE 1995 1 2.958820
# 23 BE 1995 2 1.571759
# 24 BE 1995 3 1.116049
# 25 BE 1995 4 1.888952
# 26 BE 1995 5 7.654881
# 27 BE 1995 Residual -13.643015
# 28 BE 1995 Total 1.547446
I think there are multiple ways you can do this. What I may recommend is to take advantage of the tidyverse suite of packages which includes dplyr.
Without getting too far into what dplyr and tidyverse can achieve, we can talk about the power of dplyr's inline commands group_by(...), summarise(...), arrange(...) and bind_rows(...) functions. Also, there are tons of great tutorials, cheat sheets, and documentation on all tidyverse packages.
Although it is less and less relevant these days, we generally want to avoid for loops in R. Therefore, we will create a new data frame which contains all of the Residual values then bring it back into your original data frame.
Step 1: Calculating all residual values
We want to calculate the sum of UN values, grouped by country and year. We can achieve this by this value
res_UN = UN_ %>% group_by(country, year) %>% summarise(UN = sum(UN, na.rm = T))
Step 2: Add sector column to res_UN with value 'residual'
This should yield a data frame which contains country, year, and UN, we now need to add a column sector which the value 'Residual' to satisfy your specifications.
res_UN$sector = 'Residual'
Step 3 : Add res_UN back to UN_ and order accordingly
res_UN and UN_ now have the same columns and they can now be added back together.
UN_ = bind_rows(UN_, res_UN) %>% arrange(country, year, sector)
Piecing this all together, should answer your question and can be achieved in a couple lines!
TLDR:
res_UN = UN_ %>% group_by(country, year) %>% summarise(UN = sum(UN, na.rm = T))`
res_UN$sector = 'Residual'
UN_ = bind_rows(UN_, res_UN) %>% arrange(country, year, sector)
I am very new to R so I am not sure how basic my question is, but I am stuck at the following point.
I have data that has a panel structure, similar to this
Country Year Outcome Country-characteristic
A 1990 10 40
A 1991 12 40
A 1992 14 40
B 1991 10 60
B 1992 12 60
For some reason I need to put this in a cross-sectional structure such I get averages over all years for each country, that is in the end, it should look like,
Country Outcome Country-Characteristic
A 12 40
B 11 60
Has anybody faced a similar problem? I was playing with lapply(table$country, table$outcome, mean) but that did not work as I wanted it.
Two tips: 1- When you ask a question, you should provide a reproducible example for the data too (as I did with read.table below). 2- It's not a good idea to use "-" in column names. You should use "_" instead.
You can get a summary using the dplyr package:
df1 <- read.table(text="Country Year Outcome Countrycharacteristic
A 1990 10 40
A 1991 12 40
A 1992 14 40
B 1991 10 60
B 1992 12 60", header=TRUE, stringsAsFactors=FALSE)
library(dplyr)
df1 %>%
group_by(Country) %>%
summarize(Outcome=mean(Outcome),Countrycharacteristic=mean(Countrycharacteristic))
# A tibble: 2 x 3
Country Outcome Countrycharacteristic
<chr> <dbl> <dbl>
1 A 12 40
2 B 11 60
We can do this in base R with aggregate
aggregate(.~Country, df1[-2], mean)
# Country Outcome Countrycharacteristic
#1 A 12 40
#2 B 11 60
My data is available in monthly frequency and I'm trying to aggregate them in quarterly frequency. I'm working with data.table which package I dont understand very well, to be honest.
X.DATA_BASE NOME_INSTITUICAO SALDO.x SALDO.y
1: 199407 ASB S/A - CFI 1694581 1124580
2: 199407 BANCO ARAUCARIA S.A. 40079517 6314782
3: 199407 BANCO ATLANTIS S.A. 200463907 9356445
4: 199407 BANCO BANKPAR 1078342 5770046
5: 199407 BANCO BBI 97812975 31112289
For each date, which is defined by X.DATA_BASE, 199407 = July 1994. I have several institutions with SALDO.x and SALDO.y values. I want to add SALDO.x and SALDO.y for each institution in each quarterly. One of the problem is that some institutions get in and get out through the time. In the end of the day I want to have mydata with the same columns but quarterly frequency.
How could I do that?
Here's an example of how to group and sum by quarter (with thanks to #eddi for his suggested improvement). First let's create some fake date:
library(data.table)
set.seed(1485)
dat = data.table(date=rep(c(199401:199412,199501:199512),2),
firm=rep(c("A","B"), each=24),
value1=rnorm(48,1000,10),
value2=rnorm(48,2000,100))
dat
date firm value1 value2
1: 199401 A 1009.8620 2054.251
2: 199402 A 1009.7180 2124.202
3: 199403 A 1014.3421 1919.251
...
46: 199510 B 992.9961 2079.517
47: 199511 B 997.9147 1968.676
48: 199512 B 1002.5993 2006.231
Now, summarize by firm, year, and quarter. To do this, we create year and quarter grouping variables from date (we use integer division (%/%) to create the years and mod (%%) plus integer division to create the quarters), and calculate the sum of value1 and value2 for each sub-group. This all assumes date is numeric. If you have it stored as character or factor, convert to numeric first:
dat.summary = dat[ , list(valueByQuarter = sum(sum(value1) + sum(value2))),
by=list(firm,
year=date %/% 100,
quarter=(date %% 100 - 1) %/% 3 + 1)]
dat.summary
firm year quarter valueByQuarter
1: A 1994 1 9131.626
2: A 1994 2 8953.116
3: A 1994 3 8981.407
4: A 1994 4 9175.959
5: A 1995 1 9003.225
6: A 1995 2 8962.690
7: A 1995 3 8809.256
8: A 1995 4 8885.264
9: B 1994 1 9000.791
10: B 1994 2 8936.356
11: B 1994 3 8905.789
12: B 1994 4 8951.369
13: B 1995 1 8922.716
14: B 1995 2 9097.134
15: B 1995 3 8724.188
16: B 1995 4 9047.934
For dplyr fans, here's a dplyr approach:
library(dplyr)
dat %>%
group_by(firm, year=date %/% 100,
quarter=(date %% 100 - 1) %/% 3 + 1) %>%
summarise(valueByQuarter = sum(value1 + value2))